Author:
Yang Na,Zhang Zhenkai,Yang Jianhua,Hong Zenglin
Funder
Key Science and Technology Program of Shaanxi Province
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Reference63 articles.
1. Abedi, M., Norouzi, G., & Bahroudi, A. (2012). Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences, 46, 272–283.
2. Cai, H., Chen, S., Xu, Y., Li, Z., Ran, X., Wen, X., Li, Y., & Men, Y. (2021). Intelligent recognition of ore-forming anomalies based on multisource data fusion: A case study of the Daqiao mining area, Gansu Province, China. Earth and Space Science, 8(11), 1–12.
3. Cai, H., Xu, Y., Li, Z., Cao, H., Feng, Y., Chen, S., & Li, Y. (2019). The division of metallogenic prospective areas based on convolutional neural network model: A case study of the Daqiao gold polymetallic deposit. Geological Bulletin of China, 38(12), 1999–2009.
4. Carranza, E. J. M., & Laborte, A. G. (2015a). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of random forests algorithm. Ore Geology Reviews, 71, 777–787.
5. Carranza, E. J. M., & Laborte, A. G. (2015b). Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Computers & Geosciences, 74, 60–70.
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